ISA 444: Business Forecasting

class: center, middle, inverse, title-slide

.title[
# ISA 444: Business Forecasting
]
.subtitle[
## 02: A Quick Introduction to <svg viewBox="0 0 581 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg>, <svg viewBox="0 0 448 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"></path></svg>, and LLMs
]
.author[
### <br>Fadel M. Megahed, PhD <br><br> Endres Associate Professor <br> Farmer School of Business<br> Miami University<br><br> <a href="https://twitter.com/FadelMegahed"><svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"></path></svg> <span class="citation">@FadelMegahed</span></a> <br> <a href="https://github.com/fmegahed/"><svg viewBox="0 0 496 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"></path></svg> fmegahed</a> <br> <a href="mailto:fmegahed@miamioh.edu"><svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"></path></svg> fmegahed@miamioh.edu</a><br> <a href="https://calendly.com/fmegahed"><svg viewBox="0 0 384 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M202.021 0C122.202 0 70.503 32.703 29.914 91.026c-7.363 10.58-5.093 25.086 5.178 32.874l43.138 32.709c10.373 7.865 25.132 6.026 33.253-4.148 25.049-31.381 43.63-49.449 82.757-49.449 30.764 0 68.816 19.799 68.816 49.631 0 22.552-18.617 34.134-48.993 51.164-35.423 19.86-82.299 44.576-82.299 106.405V320c0 13.255 10.745 24 24 24h72.471c13.255 0 24-10.745 24-24v-5.773c0-42.86 125.268-44.645 125.268-160.627C377.504 66.256 286.902 0 202.021 0zM192 373.459c-38.196 0-69.271 31.075-69.271 69.271 0 38.195 31.075 69.27 69.271 69.27s69.271-31.075 69.271-69.271-31.075-69.27-69.271-69.27z"></path></svg> Automated Scheduler for Office Hours</a><br><br>
]
.date[
### Fall 2023
]

---

# Quick Refresher from Last Class

✅ Describe course **objectives** & **structure**.

✅ Explain the differences between **cross sectional**, **time series** and **panel** datasets.

✅ Describe the **components of time series** datasets.

✅ Explain the **forecasting steps**.

---

# Learning Objectives for Today's Class

- Utilize the project workflow in RStudio (we will try to use that as an IDE for <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> and <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>).

- Understand the syntax, data structures and functions in both <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> and <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>.

- Understand the potential impact of LLMs on businesses and explore how they can be leveraged in the context of this class.

---

# How to Learn Any Programming Language

* 🗣 **Get hands dirty** ‼️

* 📖 Documentation! Documentation! Documentation!

* 🔍 (Not surprisingly) Learn to Google/ChatGPT: what that error message means (I do that a lot 🤣)

.footnote[
<html>

<hr>

</html>

**Source:** Slide is based on [Kia Ora's How I Learn a Technology](https://stats220.earo.me/01-intro.html#7).
]

---

class: inverse center middle

# The RStudio Interface, Setup and a Project-Oriented Workflow for your Analysis

---

## RStudio Interface

.center[<img src="../../figures/rstudio-interface.png" width="80%">]

.footnote[
<html>
<hr>
</html>
image credit: Stuart Lee]

???

live

---

## Setting up RStudio (do this once for <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>)

.pull-left[
Go to **Tools** > **Global Options**:
.center[<img src="../../figures/rstudio-setup.PNG" width="100%">]
]
.pull-right[
<br>
<br>
<br>
<br>
Uncheck `Workspace` and `History`, which helps to keep <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> working environment fresh and clean every time you switch between projects.
]

---

## What is a project?

* Each university course is a project, and get your work organised.
* A self-contained project is a folder that contains all relevant files, for example my `ISA 444/` 📂 includes:
   + `isa444.Rproj`
   + `lectures/`  
     +  `01_Introduction/`  
          *  `01_intro.Rmd`, etc.   
     +  `02_llms_r_python/`  
          *  `02_llms_r_python.Rmd`, etc. 
* All working files are **relative** to the **project root** (i.e. `isa444/`).
* The project should just work on a different computer (in most cases).

---

## Setting up RStudio (do this once for <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>)

.pull-left-3[

.font70[

.center[**Installing Python**]

1. (Preferred) Install <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg> via `reticulate::install_miniconda()`.

2. Install <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>  from [python.org](https://www.python.org/downloads/).

3. Setup virtual environment and install needed <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:gold;overflow:visible;position:relative;"><path d="M50.7 58.5L0 160H208V32H93.7C75.5 32 58.9 42.3 50.7 58.5zM240 160H448L397.3 58.5C389.1 42.3 372.5 32 354.3 32H240V160zm208 32H0V416c0 35.3 28.7 64 64 64H384c35.3 0 64-28.7 64-64V192z"/></svg>

.center[**Helping R Find the Correct Version of Python**]

1. Edit your RProfile for the project to connect to a specific version of Python,
.font70[`Sys.setenv(RETICULATE_PYTHON = "C:\\tools\\Anaconda3\\envs\\isa444\\python.exe")`]

2. Configure a default version of Python to be used with RStudio via Tools -> Global Options... -> Python
]
]

.pull-right-3[
<iframe src="python_with_r_studio.html" width="100%" height="470px" data-external="1"></iframe>
]

.footnote[
<html>
<hr>
</html>

**Note:** Feel free to use other IDEs of your choice for Python and/or R. During class, I will use RStudio for both to reduce the possible friction from using and/or setting up multiple IDEs.
]

---

## Lets Create a `.Rproj` for Our Course

.pull-left-2[
1. Click the **Project** icon on the top right corner 
<br>
<br>
<br>
<br>
2. **New Directory**/**Existing Directory** > **New Project** > **Create Project**
<br>
<br>
<br>
3. Open the project
]
.pull-right-2[
.center[<img src="../../figures/rstudio-proj1.png" width = "100%">]
]

---
class: inverse, center, middle

# 101: Syntax, Data Types, Data Structures and Functions

---

# Arthimetic Operators

The operators below are good to know.

<table border="1">
  <tr>
    <th>R</th>
    <th>Description</th>
    <th>Python</th>
  </tr>
  <tr>
    <td>+</td>
    <td>addition</td>
    <td>+</td>
  </tr>
  <tr>
    <td>-</td>
    <td>subtraction</td>
    <td>-</td>
  </tr>
  <tr>
    <td>*</td>
    <td>multiplication</td>
    <td>*</td>
  </tr>
  <tr>
    <td>/</td>
    <td>division</td>
    <td>/</td>
  </tr>
  <tr>
    <td>^ or **</td>
    <td>exponentiation</td>
    <td>**</td>
  </tr>
  <tr>
    <td>x %% y</td>
    <td>modulus (x mod y)</td>
    <td>x % y</td>
  </tr>
  <tr>
    <td>x %/% y</td>
    <td>integer division</td>
    <td>x // y</td>
  </tr>
</table>

</center>
</html>

---

# Logical Operators

Logical operators are operators that return `TRUE` (`True` <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>) and `FALSE` (`False` in <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>) values.

<table border="1">
  <tr>
    <th>R</th>
    <th>Description</th>
    <th>Python</th>
  </tr>
  <tr>
    <td>&lt;</td>
    <td>less than</td>
    <td>&lt;</td>
  </tr>
  <tr>
    <td>&lt;=</td>
    <td>less than or equal to</td>
    <td>&lt;=</td>
  </tr>
  <tr>
    <td>&gt;</td>
    <td>greater than</td>
    <td>&gt;</td>
  </tr>
  <tr>
    <td>&gt;=</td>
    <td>greater than or equal to</td>
    <td>&gt;=</td>
  </tr>
  <tr>
    <td>==</td>
    <td>exactly equal to</td>
    <td>==</td>
  </tr>
  <tr>
    <td>!=</td>
    <td>not equal to</td>
    <td>!=</td>
  </tr>
  <tr>
    <td>!x</td>
    <td>Not x</td>
    <td>not x</td>
  </tr>
  <tr>
    <td>x &amp; y</td>
    <td>x AND y</td>
    <td>x &amp; y</td>
  </tr>
  <tr>
    <td>isTRUE(x)</td>
    <td>test if X is TRUE</td>
    <td>x is `True`</td>
  </tr>
</table>

---

# Coding Style

> .font150[Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread.
 <br> -- [The tidyverse style guide](https://style.tidyverse.org)]

.pull-left[
### <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> style guide

✅ `snake_case`

]
.pull-right[
### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg> style guide

✅ `PascalCase` (Python)
]

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#34)
]

---

.center[<img src="https://d33wubrfki0l68.cloudfront.net/8a3d360c80da1186b1373a0ff0ddf7803b96e20d/254c6/diagrams/vectors/atomic.png" width="60%">]

- To check the **type of** an object in <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>, you can use the function `typeof`:

```r
x_vec = c(1,5,7)
typeof(x_vec)
```

```
## [1] "double"
```

.footnote[
<html>
<hr>
</html>
**Source:** The image is from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html)
]

---

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#../../figures/four_popular_data_types.png" alt="The four data types that we will utilize the most in our course." width="100%" />
<p class="caption">The four data types that we will utilize the most in our course.</p>
</div>

---

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#../../figures/legos-jbryan-types.png" alt="A visual representation of different types of atomic vectors" width="100%" />
<p class="caption">A visual representation of different types of atomic vectors</p>
</div>

.footnote[
<html>
<hr>
</html>
**Source:** The images are from the excellent [lego-rstats GitHub Repository by Jenny Bryan](https://github.com/jennybc/lego-rstats#readme)
]

---

Each of the four primary types has a special syntax to create an individual value:

- Logicals can be written in full (`TRUE` or `FALSE`), or abbreviated (`T` or `F`).

- Doubles can be specified in decimal (`0.1234`), scientific (`1.23e4`), or hexadecimal (`0xcafe`) form.   
    * There are three special values unique to doubles: `Inf`, `-Inf`, and `NaN` (not a number).   
    * These are special values defined by the floating point standard.

- Integers are written similarly to doubles but must be followed by `L`(`1234L`, `1e4L`, or `0xcafeL`), and can not contain fractional values.

- Strings are surrounded by `"` (e.g., `"hi"`) or `'` (e.g., `'bye'`). Special characters are escaped with `\` see `?Quotes` for full details.

.footnote[
<html>
<hr>
</html>
**Source:** The content of this slide is verbatim from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html#scalars)
]

---

# Translating <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> Data Types to <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>

<html>
<center>
<table border="1">
  <tr>
    <th>R Data Type</th>
    <th>Description</th>
    <th>Python Equivalent</th>
  </tr>
  <tr>
    <td>numeric</td>
    <td>Decimal numbers</td>
    <td>float</td>
  </tr>
  <tr>
    <td>integer</td>
    <td>Whole numbers</td>
    <td>int</td>
  </tr>
  <tr>
    <td>character</td>
    <td>Text or strings</td>
    <td>str</td>
  </tr>
  <tr>
    <td>factor</td>
    <td>Categorical data</td>
    <td>pandas.Categorical or str</td>
  </tr>
  <tr>
    <td>Date</td>
    <td>Date values</td>
    <td>datetime.date</td>
  </tr>
  <tr>
    <td>POSIXct</td>
    <td>Date and time</td>
    <td>datetime.datetime</td>
  </tr>
    <tr>
    <td>logical</td>
    <td>Boolean (TRUE/FALSE)</td>
    <td>bool</td>
  </tr>
  <tr>
    <td>complex</td>
    <td>Complex numbers</td>
    <td>complex</td>
  </tr>
</table>
</center>
</html>

---

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#../../figures/legos-jbryan-types.png" alt="A visual representation of different types of atomic vectors" width="100%" />
<p class="caption">Keeping the  visual representation of different types of atomic vectors in your head!!</p>
</div>

```r
dept = c('ACC', 'ECO', 'FIN', 'ISA', 'MGMT')
nfaculty = c(18L, 19L, 14L, 25L, 22L)
```

.footnote[
<html>
<hr>
</html>
**Source:** The images are from the excellent [lego-rstats GitHub Repository by Jenny Bryan](https://github.com/jennybc/lego-rstats#readme)
]

---

.center[<img src="../../figures/legos-jbryan-structures.png" width="92%">]

.footnote[
<html>
<hr>
</html>
**Source:** The images are from the excellent [lego-rstats GitHub Repository by Jenny Bryan](https://github.com/jennybc/lego-rstats#readme)  
]

---

```r
library(tibble)

fsb_tbl <- tibble(
  department = dept, 
  count = nfaculty, 
  percentage = count / sum(count))
fsb_tbl
```

```
## # A tibble: 5 × 3
##   department count percentage
##   <chr>      <int>      <dbl>
## 1 ACC           18      0.184
## 2 ECO           19      0.194
## 3 FIN           14      0.143
## 4 ISA           25      0.255
## 5 MGMT          22      0.224
```

---

An object contains elements of **different data types**.

.center[<img src="../../figures/legos-jbryan-list.png" width="25%">]

.footnote[
<html>
<hr>
</html>
**Source:** The image is adapted from the excellent [lego-rstats GitHub Repository by Jenny Bryan](https://github.com/jennybc/lego-rstats/blob/master/lego-rstats_014.jpg)
]

---

.center[<img src="https://d33wubrfki0l68.cloudfront.net/9628eed602df6fd55d9bced4fba0a5a85d93db8a/36c16/diagrams/vectors/list.png" width="100%">]

```r
lst <- list( # list constructor/creator
* 1:3, # atomic double/numeric vector  of length = 3
* "a", # atomic character vector of length = 1 (aka scalar)
* c(TRUE, FALSE, TRUE), # atomic logical vector of length = 3
* c(2.3, 5.9) # atomic double/numeric vector of length =3
)
lst # printing the list
```

```
## [1] "1:3"                  "a"                    "c(TRUE, FALSE, TRUE)"
## [4] "c(2.3, 5.9)"
```

.footnote[
<html>
<hr>
</html>
**Source:** Image is from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html#lists)
]

---

.pull-left[
### data type

```r
typeof(lst) # primitive type
```

```
## [1] "list"
```
### data class

```r
class(lst) # type + attributes
```

```
## [1] "list"
```
]
.pull-right[
### data structure

```r
str(lst)
# sublists can be of diff lengths and types
```

```
## List of 4
##  $ : int [1:3] 1 2 3
##  $ : chr "a"
##  $ : logi [1:3] TRUE FALSE TRUE
##  $ : num [1:2] 2.3 5.9
```
]

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/02-import-export.html#6).
]

---

A list can contain other lists, i.e. **recursive**

```r
# a named list
str(
* list(first_el = lst, second_el = iris)
  )
```

```
## List of 2
##  $ first_el :List of 4
##   ..$ : int [1:3] 1 2 3
##   ..$ : chr "a"
##   ..$ : logi [1:3] TRUE FALSE TRUE
##   ..$ : num [1:2] 2.3 5.9
##  $ second_el:'data.frame':	150 obs. of  5 variables:
##   ..$ Sepal.Length: num [1:150] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##   ..$ Sepal.Width : num [1:150] 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##   ..$ Petal.Length: num [1:150] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##   ..$ Petal.Width : num [1:150] 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##   ..$ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
```

---

.pull-left[
Subset by `[]`

```r
lst[1]
```

```
## [[1]]
## [1] 1 2 3
```
]
.pull-right[
Subset by `[[]]`

```r
lst[[1]]
```

```
## [1] 1 2 3
```
]

.center[<img src="../../figures/pepper.png" width="50%">]

.footnote[
<html>
<hr>
</html>
**Sources:** The slide is based on [Earo Wang's STAT 220 slides](https://stats220.earo.me/02-import-export.html#10) and image is from [Hadley Wickham's Tweet on Indexing lists in R](https://twitter.com/hadleywickham/status/643381054758363136?lang=en).
]

---

A matrix is a **2D data structure** made of **one/homogeneous data type.**

.pull-left[

```r
x_mat = matrix( sample(1:10, size = 4), nrow = 2, ncol = 2 ) 
str(x_mat) # its structure?
```

```
##  int [1:2, 1:2] 7 10 8 9
```

```r
x_mat # printing it nicely
print('-----------------')
*x_mat[1, 2] # subsetting
```

```
##      [,1] [,2]
## [1,]    7    8
## [2,]   10    9
## [1] "-----------------"
## [1] 8
```

]

.pull-right[

```r
x_char = matrix(
  sample(letters, size = 12), nrow = 3, ncol =4)
x_char
```

```
##      [,1] [,2] [,3] [,4]
## [1,] "p"  "a"  "e"  "j" 
## [2,] "g"  "m"  "i"  "q" 
## [3,] "k"  "v"  "u"  "z"
```

```r
*x_char[1:2, 2:3] # subsetting
```

```
##      [,1] [,2]
## [1,] "a"  "e" 
## [2,] "m"  "i"
```
]

---

.center[<img src="https://d33wubrfki0l68.cloudfront.net/9ec5e1f8982238a413847eb5c9bbc5dcf44c9893/bc590/diagrams/vectors/summary-tree-s3-2.png" width="22%">]

> .font150[If you do data analysis in R, you’re going to be using data frames. A data frame is a named list of vectors with attributes for `(column)` `names`, `row.names`, and its class, “data.frame”.  -- [Hadley Wickham](https://adv-r.hadley.nz/vectors-chap.html#list-array)]

.footnote[
<html>
<hr>
</html>
**Source:** Image is from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html#list-array)
]

---

```r
df1 <- data.frame(x = 1:3, y = letters[1:3])
typeof(df1) # showing that its a special case of a list
```

```
## [1] "list"
```

```r
attributes(df1) # but also is of class data.frame
```

```
## $names
## [1] "x" "y"
## 
## $class
## [1] "data.frame"
## 
## $row.names
## [1] 1 2 3
```

In contrast to a regular list, a data frame has **an additional constraint: the length of each of its vectors must be the same.** This gives data frames their **rectangular structure.**

---

As noted in the creation of `df1`, columns in a data frame can be of different types. Hence, it is more widely used in data analysis than matrices.

.center[<img src="../../figures/legos-jbryan-dataframe-w-text.png" width="40%">]

---

> Tibble is a **modern reimagining of the data frame**. Tibbles are designed to be (as much as possible) **drop-in replacements for data frames** that fix those frustrations. A concise, and fun, way to summarise the main differences is that tibbles are **lazy and surly: they do less and complain more**. -- [Hadley Wickham](https://adv-r.hadley.nz/vectors-chap.html#list-array)

.pull-left[[<img src="https://d33wubrfki0l68.cloudfront.net/565916198b0be51bf88b36f94b80c7ea67cafe7c/7f70b/cover.png" height="320px">](https://adv-r.hadley.nz)]

To learn more about the basics of tibble, please consult the reference below: 
* [Data frames and tibbles (Click and read from 3.6 up to and including 3.6.5)](https://adv-r.hadley.nz/vectors-chap.html#list-array)

---

# Translating <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> Data Structures to <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>

<html>
<center>
<table border="1" style="font-size: 0.8em;">
  <tr>
    <th>R Data Structure</th>
    <th>Description</th>
    <th>R Subsetting<br>(Multiple Methods)</th>
    <th>Python Equivalent</th>
    <th>Python Subsetting<br>(Multiple Methods)</th>
  </tr>
  <tr>
    <td>Vector</td>
    <td>1D array, single type</td>
    <td>vector[index]<br>vector[c(1,2)]<br>vector[-1]</td>
    <td>List (with single type)<br>or NumPy array</td>
    <td>list[-1]<br>list[1:3]<br>array[1:3]</td>
  </tr>
  <tr>
    <td>Matrix</td>
    <td>2D array, single type</td>
    <td>matrix[row, col]<br>matrix[1,]<br>matrix[,1]</td>
    <td>2D List (with single type)<br>or 2D NumPy array</td>
    <td>list[row][col]<br>array[row, col]<br>array[row,:]<br>array[:,col]</td>
  </tr>
  <tr>
    <td>Data Frame</td>
    <td>2D table,<br>multiple types</td>
    <td>df[row, col]<br>df[1,]<br>df[, "col"]<br>df$col</td>
    <td>Pandas DataFrame</td>
    <td>df.loc[row, col]<br>df.iloc[row, col]</td>
  </tr>
  <tr>
    <td>List</td>
    <td>Ordered collection,<br>multiple types</td>
    <td>list[[index]]<br>list$element_name<br>list[[1]][1]</td>
    <td>List</td>
    <td>list[index]<br>list[index][subindex]</td>
  </tr>
  <tr>
    <td>Dictionary</td>
    <td>Key-value pairs</td>
    <td>list$element_name</td>
    <td>Dictionary</td>
    <td>dict[key]<br>dict.get(key)</td>
  </tr>
</table>

</center>
</html>

---

A function call consists of the **function name** followed by one or more **argument** within parentheses.

```r
temp_high_forecast = c(86, 84, 85, 89, 89, 84, 81)
mean(x = temp_high_forecast)
```

```
## [1] 85.42857
```

* function name: `mean()`, a built-in R function to compute mean of a vector
* argument: the first argument (LHS `x`) to specify the data (RHS `temp_high_forecast`)

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#41)
]

---

Check the function's help page with `?mean`

### Class Activity

> _Please take 2 minutes to investigate the help page for `mean` in R Studio._

```r
mean(x = temp_high_forecast, trim = 0, na.rm = FALSE, ...)
```

* Read **Usage** section
  + What arguments have default values?
* Read **Arguments** section
  + What does `trim` do?
* Run **Example** code

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#42)
]

---

.pull-left[
### Match by **positions**

```r
mean(temp_high_forecast, 0.1, TRUE)
```

```
## [1] 85.42857
```
]

.pull-right[
### Match by **names**

```r
mean(x = temp_high_forecast, trim = 0.1, na.rm = TRUE)
```

```
## [1] 85.42857
```
]

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#43)
]

---

# Use Functions from Packages

.pull-left[

```r
library(dplyr)
cummean(temp_high_forecast)
```

```
## [1] 86.00000 85.00000 85.00000 86.00000 86.60000 86.16667 85.42857
```

```r
first(temp_high_forecast)
```

```
## [1] 86
```

```r
last(temp_high_forecast)
```

```
## [1] 81
```
]
.pull-right[
<br>
<br>
<br>
<br>
.center[
<img src="https://hbctraining.github.io/Intro-to-R-flipped/img/install_vs_library.jpeg" height="240px">
]
]

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#44)
]

---

# Write Your Own <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> Functions

```r
# function_name <- function(arguments) {
#   function_body
# }
my_mean <- function(x, na.rm = FALSE) {
  summation <- sum(x, na.rm = na.rm)
  summation / length(x)
}

my_mean(temp_high_forecast)
```

```
## [1] 85.42857
```

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#45)
]

---

# Write Your Own <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg> Functions

```python
# Translating the R function to Python
def my_mean(x, na_rm=False):
    """
    Calculate the mean of a list, with an option to ignore NaN values.

Parameters:
    x (list): List of numbers
    na_rm (bool): Whether to remove NaN values before calculating the mean

Returns:
    float: mean of the list
    """
    if na_rm:
        x = [i for i in x if i is not None]
        
    summation = sum(x)
    return summation / len(x)

# Test the function with a list containing None values
temp_high_forecast = [86, 84, 85, None, 89, 84, 81]
my_mean(temp_high_forecast, na_rm=True)
```

```
## 84.83333333333333
```

---
class: inverse, center, middle

# <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> and <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg> for time series analysis

---

.footnote[
<html>
<hr>
</html>

**Comment:** <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> facilitates the work with time series data by storing it in an aptly named `ts` object. 
]

---

# Demo: `Nile` & `AirPassengers` Data

In class, we will create our first <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> script where we will examine one of these two built-in datasets. In our exploration, we will:

- Examine the `typeof()` the dataset.

- Examine the `class()` of the dataset.

- Examine the `length()` of the dataset.

- `print()` the data set and examine its `frequency()`.

- Subset the data using `window()` and non ts-based sub-setting techniques.  
  + Useful for the concept of the entire time series vs a snippet that we discussed in [Class 01](fmegahed.github.io/isa444/fall2023/class01/01_intro.html?panelset1=activity2#27).

---
class: inverse, center, middle

# Generative AI: Large Language Models

---

# Background: Artificial Intelligence

.pull-left[
[A working definition](https://www.brookings.edu/articles/what-is-artificial-intelligence/) for AI

.content-box-gray[
.bold[.red[Artificial Intelligence (AI):]] .bold[A system that acts in a way, where people might denote as "intelligent" if another human were to do something similar.]
]
]

.pull-right[
<img src="data:image/png;base64,#../../figures/ai_applications.png" width="100%" style="display: block; margin: auto;" />
]

.footnote[
<html>
<hr>
</html>

**Image Source:** The flowchart's content and its LaTex code were generated using ChatGPT (May 24 Version).
]

---

# Background: The Road to Generative AI

<br>

.footnote[
<html>
<hr>
</html>

**Comment:** You have been hearing about **big data** in SPC for over a decade now. We now have models that can digest and generate answers based on more than 45TB of text. 
]

---

# Background: Generative AI

.content-box-gray[
.bold[.red[Generative AI:]] .bold[The objective is to generate new content rather than analyze existing data.]
]

.font90[
-  The generated content is based on a .bold[.red[stochastic behavior embedded in generative AI models such that the same input prompts results in different content]].
- State-of-the-art generative AI models can have up to **540 billion parameters** ([PaLM](https://arxiv.org/abs/2204.02311)).  
- With the increase in model size, researchers have observed the **“emergent abilities”** of LLMs, which were **not explicitly encoded in the training**. [Examples include](https://ai.googleblog.com/2022/11/characterizing-emergent-phenomena-in.html):
  + Multi-step arithmetic,   
  + taking college-level exams, and 
  + identifying the intended meaning of a word.  
- LLMs are **foundation models** (see [Bommasani et al. 2021](https://arxiv.org/abs/2108.07258)), large pre-trained AI systems
that can be **repurposed with minimal effort across numerous domains and diverse tasks.**
]

---

# LLMs: Natural Language Based Coding

Let us break down this prompt with ChatISA.

.font100[
 > For my business forecasting class, I want you to help me use R and Python to get the OHUR data from FRED. Show me how the data can be extracted and plotted using both software. Make each code chunk correspond to one software. Use ggplot2 and plotnine for plotting. 
]

---
class: inverse, center, middle

# Recap

---

# Summary of Main Points

By now, you should be able to do the following:

- Describe how and why we use scripted languages in this course.

- Understand the potential impact of LLMs on businesses and explore how they can be leveraged in the context of this class.

---

# 📝 Review and Clarification 📝

1. **Class Notes**: Take some time to revisit your class notes for key insights and concepts.
2. **Zoom Recording**: The recording of today's class will be made available on Canvas approximately 3-4 hours after the session ends.
3. **Questions**: Please don't hesitate to ask for clarification on any topics discussed in class. It's crucial not to let questions accumulate.

---

# 📖 Required Readings 📖

#### 🤖 LLM: Prep

- [AI and the Future of Work in Statistical Quality Control: ChatSQC](https://arxiv.org/pdf/2308.13550.pdf).   
  + Read the **abstract**, **Sections 1, 4, and 5**; feel free to skim sections 2-3.   
  + Please feel free to test the app at: <https://chatsqc.fsb.miamioh.edu/>.